Senior Solutions Architect – Simulations, Clinical Sciences, Autonomous Lab

Posted 3ds ago

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Job Description

Senior Solutions Architect driving innovation with GPU-accelerated simulations for clinical sciences and autonomous labs. Collaborating with pharmaceutical companies and software builders for AI implementation and optimization.

Responsibilities:

  • Guide customers through the end-to-end adoption of GPU-accelerated AI, from requirements gathering and proof-of-concept development to deployment, integration, and ongoing optimization.
  • Architect libraries such as GPU-accelerated solvers for quantitative systems pharmacology and CPU-to-GPU migration of scientific workloads.
  • Perform low-level CUDA optimization, including custom kernels to accelerate simulation and inference workloads in drug discovery.
  • Building physical AI and robotics solutions for autonomous labs and biomanufacturing such as sim-to-real VLA pipelines, real-time control layers, and integration of perception, control, and policy stacks on NVIDIA platforms.
  • Designing and deploying biomedical agentic AI systems, such as graph-based retrieval, multi-hop clinical reasoning, and persistent agent memory.
  • Keeping up to date on AI advancements in healthcare, including domain-specific models, robotics, and agentic frameworks.
  • Engaging with life science executives, IT leaders, data scientists, and developers to drive adoption of NVIDIA AI stack.
  • Sharing findings through training sessions, white papers, blog posts, and conference talks.

Requirements:

  • MS, PhD, or equivalent experience in Computer Science, Biomedical Engineering, Computational Biology, Computational Chemistry, Robotics, or related fields with strong applied experience.
  • 8+ years of experience.
  • Proven track record in software development for AI/ML, scientific computing, GPU acceleration, or robotics applied to healthcare or life sciences.
  • Hands-on experience across at least two of the three focus areas: GPU-accelerated scientific simulation, sim-to-real robotics, and end-to-end agentic AI.
  • Proficiency in Python and AI/ML frameworks (PyTorch, LangChain, or custom).
  • Experience with C/C++ and CUDA strongly preferred.
  • Experience deploying and scaling GPU-accelerated solutions in cloud or HPC environments (OCI, AWS, Azure, or on-prem clusters).
  • Excellent communication skills with the ability to present complex technical concepts to both technical and non-technical audiences.
  • Up to 20% travel may be required for on-site customer engagements.

Benefits:

  • Eligible for equity and benefits